dzim.filter

<p><code>log(w)</code> is used as an offset variable in the linear predictor.</p>

para

control

<p>Function to implement the particle filtering method proposed by Gordsill et al. (1993).</p>

regression

Analyze count time series with excess zeros.
Two types of statistical models are supported: Markov regression by Yang et al.
(2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al.
(2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and
parameter-driven models respectively in the time series literature. The functions used for
Markov regression or observation-driven models can also be used to fit ordinary regression models
with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB)
assumption. Besides, the package contains some miscellaneous functions to compute density, distribution,
quantile, and generate random numbers from ZIP and ZINB distributions.

dzim.filter: Particle Filtering for DZIM

Description

Usage

Arguments

References

See Also